Event-triggered adaptive neural inverse optimal output-feedback control of steer-by-wire vehicle systems with prescribed performance

Abstract

In this article, an event-triggered adaptive neural network (NN) output-feedback inverse optimal control issue is investigated for the steer-by-wire vehicle (SBWV) systems. Firstly, NNs are utilized to approximate the unknown nonlinear dynamics and the auxiliary system of SBWV systems is established. Then, a NN state observer is constructed to estimate the unmeasured states. To obtain better tracking performance, the prescribed performance technique is introduced to constrain the tracking error. An event-triggered mechanism (ETM) is established to decrease the numbers of controller execution times. Subsequently, an event-triggered adaptive NN inverse optimal output-feedback control algorithm is proposed by employing the backstepping control theory. It is proved that the developed control method can not only ensure the stability of the SBWV systems, but also guarantee the tracking error does not exceed the prescribed performance bound and converges to a small neighborhood of zero. Finally, simulation results are given to verify the validity of the proposed control method.